Joseph K Canner1, Sarah R Kaslow2, Faiz Gani3, Hatim A AlSulaim4, Gregory P Prokopowicz5, Selma Pourzal6, Kimberley E Steele7. 1. Department of Surgery, Johns Hopkins Surgery Center for Outcomes Research, Johns Hopkins University School of Medicine, Baltimore, Maryland. Electronic address: jcanner1@jhmi.edu. 2. Department of Surgery, Johns Hopkins Surgery Center for Outcomes Research, Johns Hopkins University School of Medicine, Baltimore, Maryland; University of Maryland School of Medicine, Baltimore, Maryland. 3. Department of Surgery, Johns Hopkins Surgery Center for Outcomes Research, Johns Hopkins University School of Medicine, Baltimore, Maryland. 4. Department of Surgery, Johns Hopkins Surgery Center for Outcomes Research, Johns Hopkins University School of Medicine, Baltimore, Maryland; Department of Surgery, Unaizah College of Medicine, Qassim University, Qassim, Saudi Arabia. 5. Department of Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland. 6. Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland. 7. Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland; Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Abstract
BACKGROUND: Current readmission rates do not account for readmissions to nonindex hospitals and may underestimate the actual burden of readmissions. OBJECTIVE: Using a nationally representative database, we sought to characterize nonindex readmissions following bariatric surgery and identify risk factors associated with readmission to a nonindex hospital. SETTING: Patients in the United States undergoing elective bariatric surgery. METHODS: The Nationwide Readmissions Database was used to identify a weighted sample of 545,377 patients undergoing elective bariatric surgery between 2010 and 2014. Multivariable logistic regression analysis was used to identify factors associated with readmission to a nonindex hospital. RESULTS: Among all patients, 5.6% were readmitted at least once within 30 days. Within the subgroup of patients who were readmitted, 17.6% were readmitted to a different hospital than the index admission hospital. Factors independently associated with higher odds of readmission to a nonindex hospital were primary payor (Medicare: odds ratio [OR] = 1.48, 95% confidence interval [CI]: 1.24-1.75; Medicaid: OR = 1.56, 95% CI: 1.26-1.95), All Patients Refined Diagnosis Related Group severity of illness score (extreme versus minor: OR = 1.48; 95% CI: 1.04-2.09), primary procedure (laparoscopic sleeve gastrectomy versus laparoscopic gastric bypass: OR = 1.23; 95% CI: 1.05-1.44), hospital bed size (reference: small hospital, medium: OR = .52, 95% CI: .39-.70; large: OR = .47, 95% CI: .35-.63), hospital ownership (reference: private, nonprofit hospital, government: OR = 1.77, 95% CI: 1.32-2.37; private, investor-owned: OR = 1.33, 95% CI: 1.07-1.64), and hospital location (reference: metropolitan area >1 million population, metropolitan <1 million population: OR = .44, 95% CI: .34-.56; micropolitan/rural: OR = .44, 95% CI: .27-.73). CONCLUSION: Failure to account for readmissions to different hospitals may underestimate readmission rates by approximately 18%.
BACKGROUND: Current readmission rates do not account for readmissions to nonindex hospitals and may underestimate the actual burden of readmissions. OBJECTIVE: Using a nationally representative database, we sought to characterize nonindex readmissions following bariatric surgery and identify risk factors associated with readmission to a nonindex hospital. SETTING:Patients in the United States undergoing elective bariatric surgery. METHODS: The Nationwide Readmissions Database was used to identify a weighted sample of 545,377 patients undergoing elective bariatric surgery between 2010 and 2014. Multivariable logistic regression analysis was used to identify factors associated with readmission to a nonindex hospital. RESULTS: Among all patients, 5.6% were readmitted at least once within 30 days. Within the subgroup of patients who were readmitted, 17.6% were readmitted to a different hospital than the index admission hospital. Factors independently associated with higher odds of readmission to a nonindex hospital were primary payor (Medicare: odds ratio [OR] = 1.48, 95% confidence interval [CI]: 1.24-1.75; Medicaid: OR = 1.56, 95% CI: 1.26-1.95), All Patients Refined Diagnosis Related Group severity of illness score (extreme versus minor: OR = 1.48; 95% CI: 1.04-2.09), primary procedure (laparoscopic sleeve gastrectomy versus laparoscopic gastric bypass: OR = 1.23; 95% CI: 1.05-1.44), hospital bed size (reference: small hospital, medium: OR = .52, 95% CI: .39-.70; large: OR = .47, 95% CI: .35-.63), hospital ownership (reference: private, nonprofit hospital, government: OR = 1.77, 95% CI: 1.32-2.37; private, investor-owned: OR = 1.33, 95% CI: 1.07-1.64), and hospital location (reference: metropolitan area >1 million population, metropolitan <1 million population: OR = .44, 95% CI: .34-.56; micropolitan/rural: OR = .44, 95% CI: .27-.73). CONCLUSION: Failure to account for readmissions to different hospitals may underestimate readmission rates by approximately 18%.
Authors: Benjamin Clapp; Andres Vivar; Christian Castro; Jisoo Kim; Jesus Gamez; Christopher Dodoo; Brian Davis Journal: JSLS Date: 2022 Apr-Jun Impact factor: 1.789